Self-Calibrating LLM-Based Analog Circuit Sizing with Interpretable Design Equations

• URL: http://arxiv.org/abs/2604.07387v1
• Date: Wed, 08 Apr 2026 04:35:25 GMT
• Title: Self-Calibrating LLM-Based Analog Circuit Sizing with Interpretable Design Equations
• Abstract summary: We present a self-calibrating framework for analog circuit sizing.<n>A large language model (LLM) derives topology-specific analytical design equations directly from a raw circuit netlist.<n>A deterministic calibration loop extracts process-dependent parameters from a single transistor-level simulation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a self-calibrating framework for analog circuit sizing in which a large language model (LLM) derives topology-specific analytical design equations directly from a raw circuit netlist. Unlike existing AI-driven sizing methods where the model proposes parameter adjustments or reduces a search space, the LLM produces a complete Python sizing function tracing each device dimension to a specific performance constraint. A deterministic calibration loop extracts process-dependent parameters from a single transistor-level simulation, while a prediction-error feedback mechanism compensates for analytical inaccuracies. We validate the framework on six operational transconductance amplifier (OTA) topologies spanning three families at two process nodes (180 nm and 40 nm CMOS). All 12 topology-node combinations achieve all specifications, converging in 2-9 simulations for 11 of 12 cases, with one outlier requiring 16 simulations due to an extremely narrow feasible region. Despite large initial prediction errors, convergence depends on the measurement-feedback architecture, not prediction accuracy. This one-shot calibration automatically captures process-dependent variations, enabling cross-node portability without modification, retraining, or per-process characterization.

Related papers

• Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance [4.194426163381861]
  We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expected wall-clock runtime for quantum circuit simulation.<n>Our system achieves 79.5% exact threshold accuracy (91.2% within one rung) and $R2 = 0.82$ runtime correlation, with inference completing in approximately 50 ms.
  arXiv  Detail & Related papers  (2026-06-10T03:31:37Z)
• Sensor Design for Accuracy-Bounded Estimation via Maximum-Entropy Likelihood Synthesis [0.0]
  Given an error budget, we synthesize the measurement likelihood that enforces it while injecting minimal information beyond the dynamical prior.<n>The framework accommodates arbitrary discrepancies and is instantiated for Wasserstein distance, mean discrepancy, $f$divergences, moment constraints, and hybrid metrics.
  arXiv  Detail & Related papers  (2026-05-11T18:28:45Z)
• Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization [0.0]
  Exact simulation of continuous-time Markov chains (CTMCs) is essential when discreteness and noise drive system behavior, but the hard categorical event selection in Gillespie-type algorithms blocks gradient-based learning.<n>We eliminate this constraint by decoupling forward simulation from backward differentiation, with hard categorical sampling generating exact trajectories and gradients propagating through a continuous massively-parallel Gumbel-Softmax straight-through surrogate.<n>Our results enable high-dimensional parameter inference and inverse design across systems biology, chemical kinetics, physics, and related CTMC-governed domains.
  arXiv  Detail & Related papers  (2026-02-23T12:29:43Z)
• Automatic Characterization of Fluxonium Superconducting Qubits Parameters with Deep Transfer Learning [0.5932428354861273]
  In solid state systems, the parameters of individual qubits vary across the entire system.<n>Recent developed superconducting qubits, such as fluxonium or 0-pi qubits, offer improved fidelity operations but exhibit a more complex physical and spectral structure.<n>We propose a machine learning (ML)based methodology for the automatic and accurate characterization of fluxonium qubit parameters.
  arXiv  Detail & Related papers  (2025-03-15T11:51:16Z)
• An Iterative Bayesian Approach for System Identification based on Linear Gaussian Models [70.75865595489691]
  We tackle the problem of system identification, where we select inputs, observe the corresponding outputs from the true system, and optimize the parameters of our model to best fit the data.<n>Our approach only requires input-output data from the system and first-order information of the model with respect to the parameters.
  arXiv  Detail & Related papers  (2025-01-28T01:57:51Z)
• Accelerated zero-order SGD under high-order smoothness and overparameterized regime [79.85163929026146]
  We present a novel gradient-free algorithm to solve convex optimization problems. Such problems are encountered in medicine, physics, and machine learning. We provide convergence guarantees for the proposed algorithm under both types of noise.
  arXiv  Detail & Related papers  (2024-11-21T10:26:17Z)
• Postselection-free approach to monitored quantum dynamics and entanglement phase transitions [0.0]
  Measurement-induced entanglement phase transitions in monitored quantum circuits have stimulated activity in a diverse research community.<n>We present a solution by introducing a scalable protocol in $U(1)$ symmetric circuits.
  arXiv  Detail & Related papers  (2024-06-27T09:59:58Z)
• One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers [57.441926088870325]
  Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements. Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
  arXiv  Detail & Related papers  (2023-06-06T20:28:37Z)
• Variational quantum simulation of critical Ising model with symmetry averaging [0.2578242050187029]
  We investigate the use of deep multi-scale entanglement renormalization circuits as a variational ansatz for ground states of gapless systems. We find that DMERA strongly outperforms a standard QAOA-style ansatz, and that a major source of systematic error in correlation functions approximated using DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model.
  arXiv  Detail & Related papers  (2022-10-26T21:37:14Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)
• Sinkhorn Natural Gradient for Generative Models [125.89871274202439]
  We propose a novel Sinkhorn Natural Gradient (SiNG) algorithm which acts as a steepest descent method on the probability space endowed with the Sinkhorn divergence. We show that the Sinkhorn information matrix (SIM), a key component of SiNG, has an explicit expression and can be evaluated accurately in complexity that scales logarithmically. In our experiments, we quantitatively compare SiNG with state-of-the-art SGD-type solvers on generative tasks to demonstrate its efficiency and efficacy of our method.
  arXiv  Detail & Related papers  (2020-11-09T02:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.